Hey dev community!
Ever faced a project where you need to rank a list of items not based on a single metric, but on a complex set of weighted, often subjective, attributes? I recently tackled this challenge while analyzing the UK's online entertainment review market, and I wanted to share the simple, conceptual framework I built.
The problem was clear: the market is flooded with affiliate sites, and telling the good from the bad is tough. A simple "Top 5" list is useless without a transparent methodology. So, I decided to build one.
The "Tech" Stack: The TDUX Framework
This isn't about a specific language or framework, but a conceptual model I've named the TDUX Framework (Trust, Data Depth, User Experience, X-Factor). It’s a weighted algorithm designed to generate an objective score for any review platform.
Here's a breakdown of the core "modules".
Module 1: The Trust & Transparency Pillar (Weight: 35%)
This is the non-negotiable core. A site without trust is a non-starter. I broke this down into measurable signals.
def calculate_trust_score(site_object):
eeat_score = get_eeat_signals(site_object.authors, site_object.methodology_page) # 0-15 points
licensing_clarity = check_licensing_info(site_object.reviews) # 0-10 points
objectivity_score = analyze_review_balance(site_object.reviews) # 0-10 points
final_trust = (eeat_score * 0.4) + (licensing_clarity * 0.3) + (objectivity_score * 0.3)
return final_trust
The get_eeat_signals function looks for things like named authors with actual credentials, which is incredibly rare but a huge trust indicator.
Module 2: The Data Depth Pillar (Weight: 30%)
This module answers the question: "Does this site actually provide useful data, or just opinions?"
Granularity: How many data points are in a review? We're talking specifics: payout times, RTP, provider lists.
Database Size: A huge library of reviewed games/slots (I set a benchmark of 4,000+) indicates a serious data operation.
Module 3: The User Experience (UX) Pillar (Weight: 25%)
Even the best data is useless on a terrible site.
Performance: I used GTmetrix and Google's Core Web Vitals to score speed and mobile-friendliness.
UI/Nav: Clean design and intuitive filtering get max points. Cluttered, ad-filled sites are penalized.
Module 4: The X-Factor Pillar (Weight: 10%)
This rewards unique, high-value features. A player complaint system, for example, is a massive X-Factor. An active community forum is another.
The Result: A Live Implementation
After building and refining this model, I applied it to the top 15 UK market players. The results were fascinating, revealing a clear hierarchy of quality.
This entire conceptual project wasn't just a thought experiment. It's the core logic behind a live project I contribute to. We built a full-scale platform around this exact TDUX framework to bring this level of analysis to the public.
You can see the model in action and view the full data-driven rankings on our research portal: Casimo.org.
It's a live case study of applying a quantitative framework to a real-world problem.
How would you improve this scoring model? What metrics am I missing? Let me know in the comments!
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